Abstract

Medical imaging occupies a place of choice in medical diagnosis, and this place will keep growing in the future the development of new imaging modalities and with the increasing need for personalized medicine. In this context, medical image acquisition, reconstruction and analysis are key technical components, subject to intense research all over the World, to provide the medical community with the most advanced and robust methods to extract information from the fantastic existing and future image modalities.In this project, we will promote the framework of regularized linear inverse problems in medical image acquisition and reconstruction, and develop some of its essential components, in the context of two very attractive medical imaging modalities: diffusion Magnetic Resonance Imaging (dMRI) and Ultrasound (US) imaging. This project is built as a continuation and extension of two important research lines pursued at the Signal Processing Laboratory (LTS5) of EPFL, and addresses key questions in these domains in a unified methodological framework. Indeed, in our previous works both in brain connectivity analysis by dMRI and in US image reconstruction, we proposed to formulate the data/image reconstruction aspects as regularized linear inverse problems and obtained significant preliminary results.Diffusion MRI: For the last 15 years, LTS5 has pioneered the field of brain connectivity analysis by dMRI, establishing the principle of MR connectomics. Recently, we developed an additional major contribution to the field, by reformulating the dMRI white matter microstructure estimation problems into linear inverse problems, for both microstructure imaging and microstructure informed tractography. In this project, we will continue this effort by investigating some of the key issues to obtain optimal estimation, namely dictionary design and learning as well as validation. Ultrasound imaging: Although now a mature field, medical US remains a modality supported by intense research and with extensive diagnostic and therapeutic indications in routine clinical use worldwide. Even if the research is very active, the basic component of US imaging, i.e. the beamforming method called Delay-and-Sum (DAS), has been largely untouched for several decades. While being very effective thanks to its simplicity, this method is largely suboptimal. Recently, LTS5 has developed the idea of addressing US image reconstruction as a linear inverse problem. Preliminary results already demonstrate outstanding performances in 2D imaging, both in image quality and data reduction. In this project, we will continue this effort by addressing some of the key aspects of this new paradigm, that will be required for this innovative method to become effective and have the expected impact. We will first extend it to 3D US imaging, where our framework has the potential to have the strongest impact, by enabling a high image quality while drastically reducing the data requirement and therefore making this technology appropriate for a much larger diffusion in the medical community. Secondly, we will address the computational complexity of US image reconstruction through regularized inverse problems by exploring and developing the remarkably promising idea of exploiting deep neural networks for image reconstruction.At the end of this project, we will thus have developed a series of new medical image acquisition and reconstruction methods, that will allow an optimal exploitation of these two amazing technologies: dMRI and US imaging. Finally, by improving medical imaging technologies, we will play our role of engineers, and contribute to the development of enhanced medical imaging devices, serving the need of the biomedical community, to ultimately provide new tools and methods for better understanding the human body and improved treatments for patients.